https://github.com/ray-project/langchain-ray

Examples on how to use LangChain and Ray
https://github.com/ray-project/langchain-ray

fine-tuning-t5 generative-ai langchain langchain-python llm llms ray ray-air ray-distributed

Last synced: 27 days ago
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Examples on how to use LangChain and Ray

• Host: GitHub
• URL: https://github.com/ray-project/langchain-ray
• Owner: ray-project
• License: apache-2.0
• Archived: true
• Created: 2023-04-24T22:35:29.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-06-14T17:58:44.000Z (over 1 year ago)
• Last Synced: 2024-06-11T19:35:34.938Z (6 months ago)
• Topics: fine-tuning-t5, generative-ai, langchain, langchain-python, llm, llms, ray, ray-air, ray-distributed
• Language: Python
• Homepage:
• Size: 18.6 MB
• Stars: 209
• Watchers: 8
• Forks: 46
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-ray - LangChain-Ray

README

        
# 🦜️🔗 LangChain and Ray - repository of examples

© 2023, Anyscale Inc. All Rights Reserved









Welcome to the LangChain and Ray!

## Overview

LangChain and Ray are two Python libraries that are emerging as key components of the modern open source stack for LLMs (OSS LLMs). If you're a Python developer or a machine learning practitioner, these tools can be very helpful in rapidly developing LLM-based applications by making it easier to build and deploy these models.

This repo is as a go-to place for all technical examples and use cases on how to use these two libraries together.

## Examples

| Example                                  | Code                                        | Blog                                                                                                | Video                                                     |

|------------------------------------------|---------------------------------------------|-----------------------------------------------------------------------------------------------------|-----------------------------------------------------------|

| Open Source LLM Search Engine            | [![github]](open_source_LLM_search_engine/) | [![article]](https://www.anyscale.com/blog/llm-open-source-search-engine-langchain-ray)             | [![youtube]](https://www.youtube.com/watch?v=v7a8SR-sZpI) |

| Fast and Scalable Embedding Generation   | [![github]](embedding_pdf_documents/)       | [![article]](https://www.anyscale.com/blog/turbocharge-langchain-now-guide-to-20x-faster-embedding) | [![youtube]](https://www.youtube.com/watch?v=hGnZajytlac) |

| Retrival-Based Question Answering System | [![github]](open_source_LLM_retrieval_qa/)  | [![article]]()                                                                                      | [![youtube]]()                                            |

## Connect with the Ray community

You can learn and get more involved with the Ray community of developers and researchers:

* [**Ray documentation**](https://docs.ray.io/en/latest)

* [**Official Ray site**](https://www.ray.io/)

Browse the ecosystem and use this site as a hub to get the information that you need to get going and building with Ray.

* [**Join the community on Slack**](https://forms.gle/9TSdDYUgxYs8SA9e8)

Find friends to discuss your new learnings in our Slack space.

* [**Use the discussion board**](https://discuss.ray.io/)

Ask questions, follow topics, and view announcements on this community forum.

* [**Join a meetup group**](https://www.meetup.com/Bay-Area-Ray-Meetup/)

Tune in on meet-ups to listen to compelling talks, get to know other users, and meet the team behind Ray.

* [**Open an issue**](https://github.com/ray-project/ray/issues/new/choose)

Ray is constantly evolving to improve developer experience. Submit feature requests, bug-reports, and get help via GitHub issues.

* [**Become a Ray contributor**](https://docs.ray.io/en/latest/ray-contribute/getting-involved.html)

We welcome community contributions to improve our documentation and Ray framework.



[github]: https://technical-training-assets.s3.us-west-2.amazonaws.com/Generic/icons/github-icon.png "Check code here on GitHub"

[youtube]: https://technical-training-assets.s3.us-west-2.amazonaws.com/Generic/icons/youtube-logo-icon.png "Watch video walkthrough"

[article]: https://technical-training-assets.s3.us-west-2.amazonaws.com/Generic/icons/article-icon.png "Read article on the Anyscale blog"